We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input ...the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.
A number of intriguing decision scenarios revolve around partitioning a collection of objects to optimize some application specific objective function. This problem is generally referred to as the ...Object Partitioning Problem (OPP) and is known to be NP-hard. We here consider a particularly challenging version of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In SO-EPP, the target partitioning is unknown and has to be inferred purely from observing an on-line sequence of object pairs. The paired objects belong to the same partition with probability
p
and to different partitions with probability 1 −
p
, with
p
also being unknown. As an additional complication, the partitions are required to be of equal cardinality. Previously, only heuristic sub-optimal solution strategies have been proposed for SO- EPP. In this paper, we propose the first
Bayesian
solution strategy. In brief, the scheme that we propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP problems. Based on probabilistic reasoning, we are not only able to infer the underlying object partitioning with superior accuracy. We are also able to simultaneously infer
p
, allowing us to accelerate learning as object pairs arrive. Furthermore, our scheme is the first to support a wide range of constraints on the partitioning (Constrained SO-EPP). Being Bayesian, BN-EPP provides superior performance compared to existing solution schemes. We additionally introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the problem of order picking, a representative real-life application of BN-EPP.
This research investigates the impact of forklift driver behavior on energy consumption and productivity (i.e., the average number of pallet movements per operating hour) by conducting various ...statistical and regression analyses. Forklift driver behavior data are collected from actual warehouse setting to evaluate the relationships between different driving behaviors (i.e., concurrent travel and lift, travel speed, acceleration, and braking) and driver performance. The research results show that 1) drivers perform similar tasks have different driving behaviors; 2) driver productivity increases with longer durations of concurrent travel and lift; and 3) average speed is the most significant variable that affects the energy consumption.
The warehouse is often the starting point in a supply chain. Ineffectiveness at a warehouse can cascade into the rest of the supply chain, hurting the customers' experience with the business in the ...long run. Warehouse Optimization System (WOS) play a crucial role in resolving a significant market issue by optimizing warehouse space and logistical procedures, documenting merchant supply and demand patterns, and making it simpler to handle goods that are about to expire, are out of stock, are broken, or have been abandoned by customers. This issue relates to space and storage in the warehouse, due to the fact that business intelligence, which may include algorithms for anticipating sales and managing warehouse capacity, may be connected to warehouse optimization systems. A case study is described in research of a warehouse optimization system, followed by a demonstration of the problem of gaps within the racks of warehouses. The algorithm solves the problem of fitting boxes with varying dimensions into racks. By using automation and careful design, warehouse optimization increases the effectiveness of how time, space, and resources are used in a warehouse, which enhances customer satisfaction.
With the development of e-commerce logistics centers toward intelligence, e-commerce logistics centers are characterized by multiple varieties, small batches and multiple batches, so intelligent ...warehousing optimization design is particularly important. Apriori algorithm is used to mine data association rules, which is very suitable for warehouse management. Based on the Apriori algorithm, this paper studies the correlation between different commodities in the order of the smart e-commerce logistics center B, uses the Python language to mine the commodities with the principle of strong correlation, and optimizes the design of storage locations.
The development of the Internet has promoted the rapid development of e-commerce, and at the same time impacted the traditional retail industry, making it seek to combine data analysis, machine ...learning and other modern means to seek the advancement of new retail. This paper studied the two links of warehousing (belonging to supply chain) and customer purchasing behavior (belonging to consumers) in the business ecosystem, in order to put forward a feasible plan to reduce costs and improve sales profits. That was to optimize product storage in local warehouses and use customer segmentation to achieve precise marketing according to consumer consumption habits in different regions. More importantly, the role of data analysis (data statistics and data visualization analysis) and machine learning model (K-means) in the sustainable development of the retail industry was described in detail.
Implementing a successful warehouse management system from scratch contains many challenges of different nature. The most important aspect is the financial status of the company that is implementing ...the system. How many resources is the company willing to spend is always a difficult dilemma. The second important issue is the actual usefulness of the developed solution. If the system cannot be implemented in the real world, as advanced as it may be in theory, it has very little or no use at all. In this paper, the current state of the picking zone of a medium-to-large logistics company warehouse is analyzed through a real-world case study. On the top of the collected results, a method for the optimal strategic and quantitative item placement in the picking zone is constructed through the usage of fitting algorithms. One month after the method was released to production, a quality check of the system by supervising the warehouse activities of the picking zone is conducted and the results are analyzed.
Building a successful warehouse management system encompasses solving many problems of different nature to reshape the general workflow and ensure improvements in terms of resource management. In ...order for such a system to be accepted and used by a logistics company, those solutions need to be presented through a simple, adaptable and most importantly, a feasible software solution. One of the aspects that needs to be covered while building a warehouse management system is the optimal product placement in the warehouse. If the products are strategically placed, all the other improvement strategies like stock to picking zone item transfer and item picking order become more efficient and easier to implement. In this paper, tactics and issues regarding the optimal product placement in a warehouse are analyzed in detail through a real-world case study. Solutions of this problem largely differ for new, empty warehouses and for already operative warehouses not able to carry out a stocktaking process. Approaches for both possible situations are proposed and tested out on two different warehouses in a medium-to-large logistics company.
Warehousing plays an important role in logistics and supply chain. Its operating cost accounts for over 20% of the total cost of an organization, which leads to the need for warehouse optimization to ...reduce cost and increase profit. Automated guided vehicle systems (AGVS) have been used to transport materials, products, and goods in order to raise efficiency and throughput. Warehouse optimization includes minimizing vehicle's travel time, avoiding traffic congestions and deadlocks, solving storage assignment problem, etc. This paper discusses storage assignment strategies and route planning of AGVS in warehouse operation. We will derive the method to find the storage location and the optimal route for vehicles to travel in a traditional rectangular 2-block layout warehouse. Simulated results show the implementations of the algorithm.
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